Increased technology provision and learning: Giving more for nothing?
Bibliographic record
Abstract
The development of new communication technologies has led to a push for greater technology use for teaching and learning. This is most true for distance learning education, which relies heavily on new technologies. Distance learning students, however, seem to have very limited time available for studying and learning because of work and/or family commitments. This paper focuses on the actual use by distance learning students of different teaching and learning resources and their associated teaching technologies (learning tools). The organisation of one module has been conceptualised as a toolbox, encompassing all the learning tools provided to students. This toolbox also explicitly includes an embedded priority system for the examination of available learning resources, conceptualised as a traffic-light toolbox in this paper. Results from a survey on the resources actually used by students show that students are indeed time-constrained. Students consequently follow the priority system embedded into the module and do not use non-examinable resources much. This paper concludes that students’ specific needs or situations need to be considered for the design of an effective learning toolbox, as opposed to just providing a bundle of learning tools that may be effective on their own.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.018 | 0.026 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".